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AI Engineer with 2+ years in Production-Grade RAG Systems & Data Science
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven AI Engineer and Data Scientist with hands-on experience building production-grade RAG systems, LLM-powered applications, credit risk pipelines, and customer analytics solutions. Proficient across the full AI/ML stack – from data engineering and model development to FastAPI deployment and cloud infrastructure. Combines a mechanical engineering foundation with deep expertise in Python, LangChain, FAISS, XGBoost, and AWS to deliver measurable business outcomes.
Cultural Fit Analysis
The candidate's experience spans AI/ML consulting, data science internships, and various projects including RAG systems, credit risk modeling, churn prediction, customer segmentation, and A/B testing. This diversity of projects and applications demonstrates a broad interest and adaptability, which could contribute positively to cultural fit in a dynamic AI engineering environment. The target role of 'AI Engineer' aligns well with the candidate's recent experience and stated professional summary. The certifications also show a commitment to continuous learning and skill development.
Soft Skills & Operational Fit
The resume highlights a results-driven approach and the ability to work across the full AI/ML stack. Project descriptions indicate a focus on problem-solving and delivering business value. However, without psychometric or English test scores, it's difficult to assess logical reasoning, work attitude, stress handling, or team collaboration directly. The candidate's freelance and intern experience suggests adaptability and a proactive learning attitude.